[R-sig-ME] Using mixed-effects modelling to estimate between- and within-Ss variance in an effect

[Mike Lawrence]

Hi folks,

In psychology, we're often interested not only in effects, but also
their variability. This is mostly from a pragmatic perspective, where
we want to know how much time to devote to measuring a certain
phenomenon in order to reliably obtain an expected effect.
Historically, variability has been quantified with a single measure of
"reliability" (typically obtained by correlating subsets of
measurements). More recently, it has been suggested that such single
measures confound two sources of variability that are of potentially
independent interest: between-Ss variability of the effect, and
within-Ss variability of the effect. That is, we typically compute
effects based on many observations per Ss, so variability of the
effect is theoretically computable within each Ss.

Prior to my exposure to mixed-effects modelling, I used bootstrap
resampling to estimate the between- and within-Ss variabilities of the
effect, but now that I have dipped my toes into mixed-effects
modelling, I suspect that these values might be already estimated
automatically as part of the mixed-effects modelling procedures. Is
this the case, and if so, how could I obtain these estimates from,
say, the output from lmer?

Mike

-- 
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

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~ Certainty is folly... I think. ~